Natural images are the composite consequence of multiple factors related to scene structure, illumination and imaging. Human perception of natural images remains robust despite significant variation of these factors. For example, people possess a remarkable ability to recognize faces given a broad variety of facial geometries, viewpoints, head poses and lighting conditions.
Some past facial recognition systems have been developed with the aid of linear models such as principal component analysis (“PCA”), independent component analysis (“ICA”). Principal components analysis (“PCA”) is a popular linear technique that has been used in past facial image recognition systems and processes. By their very nature, linear models work best when a single-factor varies in an image formation. Thus, linear techniques for facial recognition systems perform adequately when person identity is the only factor permitted to change. However, if other factors (such as lighting, viewpoint, and viewpointsion) are also permitted to modify facial images, the recognition rate of linear facial recognition systems can fall dramatically.
Similarly, human motion is the composite consequence of multiple elements, including the action performed and a motion signature that captures the distinctive pattern of movement of a particular individual. Human recognition of particular characteristics of such movement can be robust even when these factors greatly vary. In the 1960's, the psychologist Gunnar Kohansson performed a series of experiments in which lights were attached to people's limbs, and recorded a video of the people performing different activities (e.g., walking, running and dancing). Observers of these moving light videos in which only the lights are visible were asked to classify the activity performed, and to note certain characteristics of the movements, such as a limp or an energetic/tired walk. It was observed that this task can be performed with ease, and that the observer could sometimes determine even recognize specific individuals in this manner. This may corroborate the idea that the motion signature is a perceptible element of human motion, and that the signature of a motion is a tangible quantity that can be separated from the actual motion type.
Further, the appearance of rendered surfaces in a computer-generated image may be determined by a complex interaction of multiple factors related to scene geometry, illumination, and imaging. For example, the bidirectional reflectance distribution function (“BRDF”) may account for surface microstructure at a point. One generalization of the BRDF, namely the bidirectional texture function or BTF may capture the appearance of extended, textured surfaces. The BTF may accommodate spatially varying reflectance, surface mesostructure (i.e., three-dimensional texture caused by local height variation over rough surfaces), subsurface scattering, and other phenomena over a finite region of the surface. It is typically a function of six variables (x, y, θv, φv, θi, φi), where (x, y) are surface parametric (texel) coordinates, (θv, φv) are a view direction and (θi, φi) describe the illumination direction (a.k.a. the photometric angles). Several BTF acquisition devices are known to those of ordinary skill in the art. In essence, such devices may sample the BTF by acquiring images of a surface of interest from several different viewpoints under several different illuminations. Given only sparsely sampled BTF data, image-based rendering (IBR) may be applicable to the challenging problem of rendering the appearance of a textured surface viewed from an arbitrary vantage point under arbitrary illumination. This problem has recently attracted considerable attention.
However, there is a need to overcome at least some of the deficiencies of the prior art techniques.